Typically, a map is created by observing physical features in an environment and then drawing at least some of those physical features on the map. Maps may also be created using satellite photography, aerial photography, and/or other imaging that may identify at least some of the physical features of a physical area. Maps may be supplemented by additional data, such as text, markers, symbols, different layers, and/or other information, which may enable a user to extract specific information from the map.
Today, many maps are created in digital form for use by computing devices. These maps may include interactive data and/or may be readily updated to show changes traffic speed. However, the underlying road shown in the maps is infrequently updated. In addition, the map is often a simplified version of a road that only represents the road as having a lane.
Autonomous vehicles are often guided by map data as well as sensor data received from sensors onboard the autonomous vehicles. The autonomous vehicles are often disconnected from or unable to communicate, either directly or indirectly with other vehicles, such as to share large amounts of information other than indications of traffic speed, which is often overlaid with the map data. For example, when a typical autonomous vehicle navigates a road, the autonomous vehicle may align its location with a road from map data.
The autonomous vehicle may control the vehicle in response to signals generated by sensors onboard the autonomous vehicle, such as imaging sensors, proximity sensors, distance sensors, and/or other types of sensors. The controls may include steering inputs, acceleration/deceleration inputs, and other types of control inputs. As an example of sensor use, the autonomous vehicle may determine lane information by analyzing visual imagery captured by the onboard sensors. The autonomous vehicle may then make lane change decisions based on this self-collected lane information. The autonomous vehicle may also detect obstacles, such as stalled car, pedestrians, and/or other obstacles using the onboard sensors, which may process the information locally to generate control signals that control actions to avoid the obstacles. However, when the autonomous vehicle relies solely or primarily on self-detection of the surrounding environment, the autonomous vehicle must process large amounts of data in real-time, and may be subject to false-positive detections of obstacles, which may cause the autonomous vehicle to take undesirable actions. Further, the autonomous vehicles may not optimize use of the onboard sensors, such as by concentrating the sensors to collect data from areas known to be associated with previous obstacles, since the controller of the sensors is generally unaware of any areas known to be associated with previous obstacles.